Combined positioning method and system based on real-time slip rate estimation and compensation

Abstract

The invention relates to a combined positioning method and system based on real-time slip rate estimation and compensation, and the method comprises the steps: taking the position information and speed information of a GNSS as measurement information when the GNSS is available; in the GNSS interruption period, wheel speed correction compensation is carried out, firstly, a Kalman filter is designed to calculate the longitudinal acceleration of each wheel according to yaw velocity information measured by an IMU, secondly, the slip rate of each wheel is estimated according to slip rate recursion formulas under the vehicle driving condition and the braking condition, and finally, the slip rate of each wheel is calculated according to the slip rate recursion formulas under the vehicle driving condition and the braking condition. Correcting the wheel speed information of the WSS according to the estimated slip rate to obtain compensated wheel speed information as measurement information; and the measurement information is utilized, and an error state Kalman filter is adopted to carry out measurement updating so as to realize the positioning of the vehicle. Compared with the prior art, the method has the advantages that the wheel speed information accuracy is improved, the positioning accuracy is improved, and the positioning reliability is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile positioning, and in particular to a combined positioning method and system based on real-time slip rate estimation and compensation. Background Art

[0002] The motion state of intelligent vehicles, including position, speed, and attitude, is crucial information for realizing intelligent driving. Wheel Speed ​​Sensor (WSS) is an attractive solution to improve the positioning accuracy of the Global Navigation Satellite System (GNSS) / Inertial Navigation System (INS) integrated system during GNSS outages. However, under high dynamic driving conditions, the longitudinal slip of the wheels leads to poor positioning results.

[0003] Most studies directly use the measured wheel speed information as measurement information without considering the wheel speed v when the vehicle slips. WSS The actual vehicle speed v vehicle There are errors between them. Especially in large dynamic conditions such as strong driving / strong braking, there are large errors.

[0004] The invention patent with publication number CN113985466A discloses a combined navigation method and system based on pattern recognition. The method updates the vehicle motion state according to the strapdown inertial navigation update algorithm; detects the vehicle's global navigation satellite system signal in real time. If the vehicle's global navigation satellite system signal is detected, the combined navigation model and observation quantity are selected according to the vehicle's global navigation satellite system signal, the controller local area network vehicle speed data and the three-axis angular velocity output by the inertial measurement unit, and the Kalman filter is used to predict and update the vehicle motion state according to the selected combined navigation model and observation quantity. However, this method does not consider the error between the wheel speed and the actual vehicle speed when the vehicle slips, and does not consider the influence of the slip rate on positioning, so the positioning is still not accurate enough. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a combined positioning method and system based on real-time slip rate estimation and compensation.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] According to one aspect of the present invention, a combined positioning method based on real-time slip rate estimation and compensation is provided. The method uses IMU, WSS and GNSS combined positioning, implements state estimation based on error state Kalman filtering, and estimates and compensates slip rate when GNSS is unavailable. The method steps include:

[0008] S1. When GNSS is available, the position information and speed information of GNSS are used as the measurement information; when GNSS is interrupted, the slip rate correction compensation of the wheel speed is performed to obtain the compensated wheel speed information as the measurement information;

[0009] S2. Using the measurement information, an error state Kalman filter is used to perform measurement updates to achieve vehicle positioning.

[0010] As a preferred technical solution, the slip rate correction and compensation steps of the wheel speed in S1 are:

[0011] S11, collecting wheel speed information and yaw angular velocity information;

[0012] S12, using a Kalman filter to obtain the first-order derivative of the yaw angular velocity;

[0013] S13, using the first-order derivative of the yaw angular velocity to obtain the longitudinal acceleration at each wheel;

[0014] S14, respectively obtaining a slip rate recursive formula under the vehicle driving condition and the vehicle braking condition, and substituting the longitudinal acceleration at each wheel into the formula to obtain the slip rate of each wheel;

[0015] S15. Compensate and correct the collected wheel speed information using the obtained wheel slip ratio.

[0016] As a preferred technical solution, the wheel speed information is collected in S11 by using WSS to obtain the longitudinal speed measurement information of the vehicle, thereby obtaining the wheel speed information v in the vehicle coordinate system. WSS .

[0017] As a preferred technical solution, the specific step of obtaining the first-order derivative of the yaw angular velocity by the Kalman filter in S12 is to perform a polynomial Taylor expansion on the yaw angular velocity at time t, ignoring higher-order dynamics above the second order, and the formula is:

[0018]

[0019] Among them, ω bz is the yaw angular velocity; is the first-order derivative of the yaw rate; is the second-order derivative of the yaw rate; Δt is the time interval between time k and time k-1; ξ1, ξ2 and ξ3 are random noises of each order.

[0020] As a preferred technical solution, the slip rate recursive formula under S14 vehicle driving condition is:

[0021]

[0022] The slip rate recursive formula under vehicle braking is:

[0023]

[0024] Among them, v wSs,k-1 is the wheel speed at time k-1; v WsS,k is the wheel speed at time k; a y,k-1 is the longitudinal acceleration at time k-1; Δt is the time interval between time k and time k-1.

[0025] As a preferred technical solution, the error state Kalman filter in S2 is specifically based on the state quantity X k The designed state quantity X k Includes: Position error δp k , speed error Attitude Error Accelerometer bias and the gyro bias

[0026] As a preferred technical solution, the state equation of the error state Kalman filter in S2 is:

[0027]

[0028] in, Predict the state at time k for time k-1; F is the state transition matrix; is the optimal state at time k-1; w k is the noise matrix.

[0029] As a preferred technical solution, in S2, for the measurement information during the period when GNSS is available, the measurement equation for the Kalman filter to process the measurement information is specifically:

[0030]

[0031] Among them, η k,GNSS is the measurement noise of GNSS; Z k,GNSS is the observation information of GNSS at time k; P INS is the predicted position state from INS; P GNSS is the position measurement information from GNSS; is the predicted speed state from INS; is the speed measurement information from GNSS; Hk,GNSS is the measurement matrix of GNSS information at time k.

[0032] As a preferred technical solution, in S2, for the measurement information during the GNSS interruption, the measurement equation of the Kalman filter processing the measurement information is specifically:

[0033]

[0034] H k,WSS =[0 3×3 I 3×3 0 3×9 ];

[0035]

[0036] Among them, η k,WSS is the measurement noise of WSS; Z k,WSS is the observation information of WSS at time k; is the speed measurement information from WSS; H k,WSS is the measurement matrix of WSS information at time k; Represents the rotation matrix from b system to n system; is the rotation matrix from v system to b system; It is the wheel speed information after slip rate compensation.

[0037] According to another aspect of the present invention, a combined positioning system based on real-time slip rate estimation and compensation is provided, characterized in that the system works by applying a combined positioning method based on real-time slip rate estimation and compensation as described above, and the system comprises a GNSS module, an INS module, a WSS module and a Kalman filter module;

[0038] The GNSS module is used to obtain the vehicle's location and speed information;

[0039] The INS module includes an inertial measurement unit (IMU), which is used to obtain information including vehicle acceleration and yaw rate;

[0040] The WSS module is used to obtain vehicle wheel speed information and estimate and compensate for its slip rate;

[0041] The Kalman filter module is used to process the measurement information to complete the positioning of the vehicle.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] 1. A combined positioning method based on real-time slip rate estimation and compensation in the present invention utilizes IMU, WSS and GNSS combined positioning, realizes state estimation based on error state Kalman filtering, and estimates and compensates for slip rate when GNSS is unavailable, thereby improving positioning accuracy during GNSS interruption and enhancing positioning accuracy.

[0044] 2. The present invention designs a Kalman filter based on error state; when GNSS is available, the position information and speed information of GNSS are used as measurement information; when GNSS is interrupted, the wheel speed information is corrected and compensated for the slip rate, and the compensated wheel speed information is used as measurement information; finally, the measurement information is used to perform measurement update using the error state Kalman filter to achieve vehicle positioning. This makes the positioning system still reliable during high dynamic driving, and improves the reliability of positioning.

[0045] 3. The present invention collects wheel speed information and yaw rate information, and uses the Kalman filter to obtain the first-order derivative of the yaw rate; uses the first-order derivative of the yaw rate to obtain the longitudinal acceleration of each wheel; obtains the slip rate recursive formula under the vehicle driving and braking conditions, and brings the longitudinal acceleration of each wheel into the formula to obtain the slip rate of each wheel; and uses the obtained slip rate of each wheel to compensate and correct the collected wheel speed information. This improves the accuracy of the wheel speed information, thereby improving the positioning accuracy of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of the architecture of a combined positioning method based on real-time slip rate estimation and compensation in the present invention;

[0047] Figure 2 A schematic diagram of an experimental vehicle and sensors used for verification in the embodiments;

[0048] Figure 3 A schematic diagram of the vehicle speed during a straight-line driving condition in the embodiment;

[0049] Figure 4 It is the experimental trajectory diagram of the straight-line driving condition in the embodiment;

[0050] Figure 5 This is a schematic diagram of the longitudinal acceleration estimation result during the straight-line driving condition in the embodiment;

[0051] Figure 6a Schematic diagram of the left rear wheel slip rate estimation result during straight-line driving in the embodiment;

[0052] Figure 6b Schematic diagram of the right rear wheel slip rate estimation result during straight-line driving in the embodiment;

[0053] Figure 7 Schematic diagram of horizontal position error during straight-line driving in the embodiment;

[0054] Figure 8 A schematic diagram of vehicle speed during a turning condition in an embodiment;

[0055] Fig. 9 It is the experimental trajectory diagram of the turning condition in the embodiment;

[0056] Fig.10 Schematic diagram of the longitudinal acceleration estimation result during the turning condition in the embodiment;

[0057] Fig.11a Schematic diagram of the left rear wheel slip rate estimation result during the turning condition in the embodiment;

[0058] Fig.11b Schematic diagram of the right rear wheel slip rate estimation result during the turning condition in the embodiment;

[0059] Fig.12 Schematic diagram of horizontal position error during turning conditions in the embodiment. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0061] Example

[0062] In this embodiment, in order to obtain a high-precision vehicle state estimation, especially during a Global Navigation Satellite System (GNSS) outage, the following is applied: Figure 1The method shown in the figure uses the measurements of the Inertial Navigation System (INS), Wheel Speed ​​Sensor (WSS), and GNSS as inputs, and realizes the optimal estimation of the state based on the error state Kalman filter. First, the recursive estimation of the state is realized based on the INS algorithm using the measurements of the Inertial Measurement Unit (IMU). Subsequently, during the period when GNSS is available, high-precision GNSS measurements are used to update the state. During the more critical GNSS interruption period, the vehicle's wheel speed information is used for state update; in order to improve the accuracy of wheel speed measurement under strong driving / braking conditions, an innovative slip ratio estimation and compensation method is proposed. The accuracy of the wheel speed information is significantly improved, thereby improving the positioning accuracy of the vehicle.

[0063] In this embodiment, the coordinate system used is as follows: n Represents the navigation coordinate system, which is the n-system, also known as ENU (East-North-Up). The origin coincides with the center of the IMU sensor frame. The vehicle body coordinate system is represented by (·) b It indicates that it is the b system, and its origin also coincides with the center of the IMU. (·) v Represents the vehicle coordinate system, whose origin is at the center of the vehicle's rear axle.

[0064] In this embodiment, a combined positioning method based on real-time slip rate estimation and compensation is used to position the vehicle.

[0065] In this embodiment, at time k, the vehicle state X estimated by this paper is k Includes: Position error δp k (longitude, latitude and altitude), speed error Attitude Error Accelerometer bias and the gyro bias

[0066]

[0067] In this embodiment, the INS predicted state and the measurement results are fused through the error state Kalman filter. The state equation of the Kalman filter can be expressed as:

[0068]

[0069] Where F is the state transition matrix; w k represents the noise matrix; Predict the state at time k for time k-1; is the optimal state at time k-1.

[0070] In this embodiment, when GNSS is available, the position information and speed information of GNSS are used as the measurement information; when GNSS is interrupted, the slip rate correction compensation of the wheel speed is performed to obtain the compensated wheel speed information as the measurement information;

[0071] Most studies directly treat the measured wheel speed information as a measurement without considering the wheel speed v when the vehicle is slipping. WSS The actual vehicle speed v vehicle There are errors between them. Especially in large dynamic conditions such as strong driving / strong braking, there are large errors.

[0072] In this embodiment, a real-time slip ratio estimation and compensation algorithm is proposed to provide more accurate wheel speed observation.

[0073] In this embodiment, the wheel speed sensor is used to provide the longitudinal speed measurement of the vehicle, so the wheel speed information obtained in the vehicle coordinate system is v WSS =[0v WSS 0] T .

[0074] In this embodiment, a Kalman filter is designed to estimate it. At time t, a polynomial Taylor expansion is performed on the yaw angular velocity, ignoring the higher-order dynamics above the second order, and then:

[0075]

[0076] Among them, ω bz is the yaw angular velocity; is the first-order derivative of the yaw rate; is the second-order derivative of the yaw rate; Δt is the time interval between time k and time k-1; ξ1, ξ2 and ξ3 are random noises of each order.

[0077] In this embodiment, the state equation constructed based on the above formula is as follows:

[0078]

[0079] In this embodiment, the measurement information is ω bz . Therefore, the observation equation is defined as follows:

[0080]

[0081] Among them, ω bz is the yaw angular velocity; is the first-order derivative of the yaw rate; is the second-order derivative of the yaw rate.

[0082] In this embodiment, by substituting the known quantity, the first-order derivative of the yaw angular velocity can be obtained:

[0083] In this embodiment, when the slip rate is estimated, the longitudinal acceleration a at the wheel is required. y,k-1 When the vehicle is traveling in a straight line, due to the small yaw rate, the vehicle body can be considered as a rigid body, and the longitudinal speed of each part is the same. At this time, the longitudinal acceleration at the left and right wheels uses the longitudinal acceleration information a at the IMU. b,y However, in a cornering condition, since the speeds of the left and right wheels are significantly different, the longitudinal acceleration at each wheel needs to be estimated.

[0084] In this embodiment, the longitudinal velocity at the IMU and the longitudinal velocity at the left wheel have the following conversion relationship:

[0085] v RL,y =v b,y -B L ω bz ;

[0086] In this embodiment, the longitudinal velocity at the IMU and the longitudinal velocity at the right wheel have the following conversion relationship:

[0087] v RR,y =v b,y +B R ω bz ;

[0088] In this embodiment, by taking the derivative of the above formula, the longitudinal acceleration estimation formulas at the left and right wheels can be obtained respectively:

[0089]

[0090] Among them, B L and B R are the wheelbases from IMU to the left and right wheels respectively; a b,y is the longitudinal acceleration measured by IMU; ω bz is the yaw rate measured by IMU; is the first-order derivative of the yaw rate at the IMU.

[0091] In this embodiment, when the vehicle is driven, the slip rate of a certain wheel is:

[0092]

[0093] In this embodiment, when the vehicle brakes, the slip rate of a certain wheel is:

[0094]

[0095] Among them, v vehicle is the actual vehicle speed; v WSS Wheel speed information.

[0096] Combined with the kinematic formula v vehicle,k =v vehicle,k-1 +a y,k Δt, we can derive the slip rate recursive formula under driving acceleration:

[0097]

[0098] In this embodiment, the slip ratio recursive formula under braking is:

[0099]

[0100] Among them, v WSS,k-1 is the wheel speed at time k-1; v WSS,k is the wheel speed at time k; a y,k-1 is the longitudinal acceleration at time k-1; Δt is the time interval between time k and time k-1.

[0101] In this embodiment, the wheel speed information is compensated and corrected based on the estimated slip ratio:

[0102] In this embodiment, when the vehicle is driven to accelerate,

[0103]

[0104] In this embodiment, when the vehicle brakes,

[0105]

[0106] in, is the wheel speed information after slip rate compensation; s a The wheel slip rate when the vehicle is driven; s b It is the wheel slip rate when the vehicle brakes.

[0107] In this embodiment, the measurement information is utilized and an error state Kalman filter is used to perform measurement updates to achieve positioning of the vehicle.

[0108] In this embodiment, the measurement equation of the Kalman filter is changed according to the measurement information.

[0109] In this embodiment, for the measurement information during the period when GNSS is available, the measurement equation for processing the measurement information by the Kalman filter is specifically:

[0110]

[0111] Among them, η k,GNSS is the GNSS measurement noise, Z k,GNSS is the observation information of GNSS at time k; P INS is the predicted position state from INS; P GNSSis the position measurement information from GNSS; is the predicted speed state from INS; is the speed measurement information from GNSS; H k,GNSS is the measurement matrix of GNSS information at time k.

[0112] In this embodiment, for the measurement information during the GNSS interruption, the measurement equation of the Kalman filter processing the measurement information is specifically:

[0113]

[0114] H k,WSS =[0 3×3 I 3×3 0 3×9 ];

[0115]

[0116] Among them, η k,WSS is the measurement noise of WSS; Z k,WSS is the observation information of WSS at time k; is the speed measurement information from WSS; H k,WSS is the measurement matrix of WSS information at time k; Represents the rotation matrix from b system to n system, which is obtained by the INS system. It is the rotation matrix from the v system to the b system, and is the pre-calibrated external parameter matrix. is the wheel speed information after slip rate compensation.

[0117] In this embodiment, it is not difficult to see through the formula analysis that under large dynamic conditions such as strong driving or strong braking, the difference between the wheel speed information and the actual vehicle speed is large due to the large slip rate. If this problem is not considered, it will lead to a large positioning error. However, the slip rate estimation and compensation method used in this embodiment can solve this problem well.

[0118] In this embodiment, a combined positioning method based on real-time slip rate estimation and compensation is verified in actual situations.

[0119] In this embodiment, the verification experiment uses Figure 2 The vehicle is equipped with sensors including: MEMS-IMU ASM330LHH provides acceleration and angular velocity measurements of three axes; GNSS receiver Novatel 718D provides RTK GNSS measurement information; GNSS signal is good and the accuracy can reach centimeter level.

[0120] In this embodiment, the wheel speed data is obtained from the controller area network bus interface of the vehicle. In addition, the high-precision INS / GNSS combined positioning system Novatel SPAN KVH 1750 can provide high-precision vehicle status as a true value.

[0121] In this embodiment, the Jiading campus of Tongji University was selected as the experimental site, and the experiment included braking conditions during straight driving, driving and braking conditions during turning. All were conducted in an open scene, and the GNSS was always in the Real Time Kinematic (RTK) state.

[0122] In this embodiment, the GNSS interruption situation is simulated by artificially cutting off the GNSS signal to verify the effectiveness of the positioning algorithm considering the slip rate compensation.

[0123] In this embodiment, the vehicle trajectory during the straight-line driving condition experiment is as follows: Figure 4 As shown, the blue is the experimental trajectory of the true value, the red is the experimental trajectory without compensation, and the orange is the experimental trajectory after compensating the slip rate. The experiment went through the process of stationary, acceleration and braking. We cut off the GNSS signal from 50s to simulate GNSS interruption. At this time, the vehicle is in the braking process, and the maximum braking acceleration can reach 5m / s 2 , which means the wheels will slip.

[0124] In this embodiment, the vehicle speed during the straight-line driving condition is as follows: Figure 3 The longitudinal acceleration estimation results are shown in Figure 5 As shown; the blue is the longitudinal acceleration collected by the IMU, the red is the longitudinal acceleration collected by the Novatel SPAN KVH 1750, and the orange is the longitudinal acceleration estimation result obtained by the method in this embodiment; the estimation results of the slip rate of the left and right rear wheels during straight-line driving are shown in Figure 6a , 6b As shown in the figure, the blue line is the reference true value, and the red line is the value calculated recursively by this method. It can be seen that the slip rate estimation accuracy is high; the horizontal position error is as follows Figure 7 As shown, the blue line is the horizontal position error when the slip rate is not compensated, and the red line is the horizontal position error after the slip rate is compensated. It is not difficult to see that the positioning accuracy is significantly improved after adding slip rate compensation.

[0125] In this embodiment, the vehicle trajectory during the turning condition experiment is as follows: Fig. 9As shown, the blue is the true value of the experimental trajectory, the red is the experimental trajectory without compensation, and the orange is the experimental trajectory after the slip rate is compensated. The experiment cuts off the GNSS signal 80s after the vehicle turns to simulate GNSS interruption. At this time, the vehicle has experienced intense driving and braking processes, which means that the wheels will slip.

[0126] In this embodiment, the vehicle speed during the turning condition is as follows: Figure 8 The longitudinal acceleration estimation results are shown in Fig.10 As shown; the blue is the longitudinal acceleration collected by the IMU, the red is the longitudinal acceleration collected by the Novatel SPAN KVH 1750, and the orange is the longitudinal acceleration estimation result obtained by the method in this embodiment; the estimation results of the slip rate of the left and right rear wheels during straight-line driving are shown in Fig.11a , 11b As shown in the figure, the blue line is the reference true value, and the red line is the value calculated recursively by this method. It can be seen that the slip rate estimation accuracy is high; the horizontal position error is as follows Fig.12 As shown, the blue line is the horizontal position error when the slip rate is not compensated, and the red line is the horizontal position error after the slip rate is compensated. It is not difficult to see that the positioning accuracy is significantly improved after adding slip rate compensation.

[0127] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A combined positioning method based on real-time slip rate estimation and compensation, characterized in that: The method utilizes INS, WSS and GNSS combined positioning, realizes state estimation based on error state Kalman filtering, and estimates and compensates for slip rate when GNSS is unavailable. The method steps include: S1. When GNSS is available, the position information and speed information of GNSS are used as the measurement information; when GNSS is interrupted, the slip rate correction compensation of the wheel speed is performed to obtain the compensated wheel speed information as the measurement information; S2. Using the measurement information, an error state Kalman filter is used to perform measurement updates to achieve vehicle positioning.

2. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that: The steps of slip ratio correction and compensation of wheel speed in S1 are as follows: S11, collecting wheel speed information and yaw angular velocity information; S12, using a Kalman filter to obtain the first-order derivative of the yaw angular velocity; S13, using the first-order derivative of the yaw angular velocity to obtain the longitudinal acceleration at each wheel; S14, respectively obtaining a slip rate recursive formula under the vehicle driving condition and the vehicle braking condition, and substituting the longitudinal acceleration at each wheel into the formula to obtain the slip rate of each wheel; S15. Compensate and correct the collected wheel speed information using the obtained wheel slip ratio.

3. The combined positioning method based on real-time slip rate estimation and compensation according to claim 2, characterized in that: The wheel speed information collected in S11 is specifically to obtain the longitudinal speed measurement information of the vehicle using WSS, thereby obtaining the wheel speed information v in the vehicle coordinate system. WSS .

4. The combined positioning method based on real-time slip rate estimation and compensation according to claim 2, characterized in that: The specific step of obtaining the first-order derivative of the yaw angular velocity by the Kalman filter in S12 is to perform a polynomial Taylor expansion on the yaw angular velocity at time t, ignoring higher-order dynamics above the second order, and the formula is: Among them, ω bz is the yaw angular velocity; is the first derivative of the yaw rate; is the second-order derivative of the yaw rate; Δt is the time interval between time k and time k-1; ξ1, ξ2 and ξ3 are random noises of each order.

5. The combined positioning method based on real-time slip rate estimation and compensation according to claim 2, characterized in that: The slip ratio recursive formula under the S14 vehicle driving condition is: The slip rate recursive formula under vehicle braking is: Among them, v WSS,k-1 is the wheel speed at time k-1; v WSS,k is the wheel speed at time k; a y,k-1 is the longitudinal acceleration at time k-1; Δt is the time interval between time k and time k-1.

6. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that: The error state Kalman filter in S2 is specifically based on the state quantity X k The designed state quantity X k Includes: Position error δp k , speed error Attitude Error Accelerometer bias and the gyro bias 7. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that: The state equation of the error state Kalman filter in S2 is: in, Predict the state at time k for time k-1; F is the state transition matrix; is the optimal state at time k-1; w k is the noise matrix.

8. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that: In S2, for the measurement information during the period when GNSS is available, the measurement equation for measuring and updating using the error state Kalman filter is specifically: Among them, η k,GNSS is the measurement noise of GNSS; Z k,GNSS is the observation information of GNSS at time k; P INS is the predicted position state from INS; P GNSS is the position measurement information from GNSS; is the predicted speed state from INS; is the speed measurement information from GNSS; H k,GNSS is the measurement matrix of GNSS information at time k.

9. The combined positioning method based on real-time slip rate estimation and compensation according to claim 1, characterized in that: In S2, the measurement equation for measuring and updating the measurement information during the GNSS interruption period using the error state Kalman filter is specifically: H k,WSS =[0 3×3 I 3×3 0 3×9 ]; Among them, η k,WSS is the measurement noise of WSS; Z k,Wss is the observation information of WSS at time k; is the speed measurement information from WSS; H k,WSS is the measurement matrix of WSS information at time k; Represents the rotation matrix from b system to n system; is the rotation matrix from v system to b system; It is the wheel speed information after slip rate compensation.

10. A combined positioning system based on real-time slip rate estimation and compensation, characterized in that: The system is operated by a combined positioning method based on real-time slip rate estimation and compensation as described in any one of claims 1 to 9, and the system is composed of a GNSS module, an INS module, a WSS module and a Kalman filter module; The GNSS module is used to obtain the location information and speed information of the vehicle; The INS module includes an inertial measurement unit IMU, which is used to obtain information including vehicle acceleration and yaw rate; The WSS module is used to obtain vehicle wheel speed information and estimate and compensate for slip rate; The Kalman filter module is used to process the measurement information to complete the positioning of the vehicle.

Images